New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

This article describes methods for semiautomatic thesaurus construction, for a cross generation, cross genre, and cross cultural corpus. Semiautomatic thesaurus construction is a complex task, and applying it on a cross generation corpus brings its own challenges. We used a Jewish juristic corpus containing documents and genres that were written across 2000 years, and contain a mix of different languages, dialects, geographies, and writing styles. We evaluated different first and second order methods, and introduced a special annotation scheme for this problem, which showed that first order methods performed surprisingly well. We found that in our case, improving the coverage is the more difficult task, for this we introduce a new algorithm to increase recall (coverage)—which is applicable to many other problems as well, and demonstrates significant improvement in our corpus.

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Corpus-based thesaurus construction for Morphologically Rich Languages (MRL) is a complex task, due to the morphological variability of MRL. In this paper we explore alternative term representations, complemented by clustering of morphological variants. We introduce a generic algorithmic scheme for thesaurus construction in MRL, and demonstrate the empirical benefit of our methodology for a Hebrew thesaurus.

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words.